Background: The heart rate variability (HRV) of patients with disorders of consciousness (DOC) differs from healthy individuals. However, there is rarely research on HRV among DOC patients following treatment with deep brain stimulation (DBS). This study aims to investigate the modulatory effects of DBS-on the central-autonomic nervous system of DOC based on the study of HRV variations.
Methods: We conducted DBS surgery on eight patients with DOC. Postoperatively, all patients underwent short-duration stimulation for 3 days, with stimulation frequencies of 25 Hz, 50 Hz, and 100 Hz respectively. Each day comprised four cycles, with a stimulation duration of 30 min DBS-on and 90 min DBS-off. We obtained the coma recovery scale-revised (CRS-R) scores and synchronously recorded electrocardiographic data.
Resulits: We analyzed the HRV indices, including time-domain and frequency-domain parameters across various time points for all patients. The HRV exhibited a consistent trend across the three groups with different parameters. Notably, the most pronounced HRV changes were induced by the 100 Hz. Long-term follow-up indicates that high-frequency (HF), low-frequency (LF), and total power (TP) of HRV may serve as predictive indicators in the prognosis of patients.
Conclusion: Our study reveals that DBS enhances DOC patient consciousness while increasing HRV. Specifically, frequency-domain indices correlate with favorable prognosis.
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http://dx.doi.org/10.1111/cns.70274 | DOI Listing |
Brain
March 2025
Krembil Brain Institute, University Health Network, Toronto, ON M5T 1M8, Canada.
Parkinson's disease is characterized, in part, by hypoactivity of direct pathway inhibitory projections from striatum to the globus pallidus internus (GPi) and indirect pathway inhibitory projections from globus pallidus externus (GPe) to the subthalamic nucleus (STN). In people with Parkinson's disease (n=32), we explored the potential use of intracranial stimulation for eliciting long-term potentiation (LTP) of these underactive pathways to produce improvement of symptoms that persists beyond stimulation cessation. During GPi deep brain stimulation (DBS) surgery, we found strong evidence (p<.
View Article and Find Full Text PDFSci Adv
March 2025
College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Brain age gap (BAG), the deviation between estimated brain age and chronological age, is a promising marker of brain health. However, the genetic architecture and reliable targets for brain aging remains poorly understood. In this study, we estimate magnetic resonance imaging (MRI)-based brain age using deep learning models trained on the UK Biobank and validated with three external datasets.
View Article and Find Full Text PDFGigascience
January 2025
Concordia University, Department of Computer Science and Software Engineering, 1455 Blvd. De Maisonneuve Ouest, Montreal, Quebec H3G 1M8, Canada.
Magnetic resonance imaging (MRI) preprocessing is a critical step for neuroimaging analysis. However, the computational cost of MRI preprocessing pipelines is a major bottleneck for large cohort studies and some clinical applications. While high-performance computing and, more recently, deep learning have been adopted to accelerate the computations, these techniques require costly hardware and are not accessible to all researchers.
View Article and Find Full Text PDFObjective: To enable fast and stable neonatal brain MR imaging by integrating learned neonate-specific subspace model and model-driven deep learning.
Methods: Fast data acquisition is critical for neonatal brain MRI, and deep learning has emerged as an effective tool to accelerate existing fast MRI methods by leveraging prior image information. However, deep learning often requires large amounts of training data to ensure stable image reconstruction, which is not currently available for neonatal MRI applications.
J Neurol
March 2025
Centre for Neurology, Department of Neurodegenerative Diseases, and Hertie Institute for Clinical Brain Research, University of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.
Next-generation neurostimulators capable of running closed-loop adaptive deep brain stimulation (aDBS) are about to enter the clinical landscape for the treatment of Parkinson's disease. Already promising results using aDBS have been achieved for symptoms such as bradykinesia, rigidity and motor fluctuations. However, the heterogeneity of freezing of gait (FoG) with its wide range of clinical presentations and its exacerbation with cognitive and emotional load make it more difficult to predict and treat.
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